Virtualization Of Manufacturing Resource In Cloud Manufacturing Environment

Cloud manufacturing is a new service-oriented and networked manufacturing mode, it provides a new train of thought for transformation of service for China’s manufacturing industry, but it has not reached the degree of real landing realization. As a mode of networked manufacturing, cloud manufacturing emphasizes on-demand access to the resource, and this point is very good in line with the demands of the manufacturing enterprises to comprehensive sharing of resources in the Internet age. Logical resource extraction and service is the foundation of cloud manufacturing, this thesis takes manufacturing stage of the manufacturing process in the cloud manufacturing environment as the research object. In view of the problems existing in the virtualization and service process of manufacturing resources, the thesis has conducted the thorough research from the overall research of virtualization system、manufacturing resource modeling、manufacturing resources mapping method and manufacturing resource selection and implementation.According to the characteristic of manufacturing resources, the underlying virtual architecture is built, establishing the extraction model of virtual resources, establishing the model of manufacturing resources and virtual resources by the metamodel, researching the virtual resource extraction strategy of manufacturing resources. Based on the analysis on the concept of ontology, combined with the characteristics of manufacturing resources, the thesis puts forward the formalization definition of meta-concepts and meta-properties of manufacturing resources and virtual resources, and builds the ontology construction model of manufacturing resources on the basis of OWL. It is proved to be effective through the prototype system. On this basis, the resource combination model and the composite service model are put forward.According to the features of manufacturing resources, the thesis establishes the virtualization mapping model, and analyzes the basic mapping rules and the process realization, and researches premise of the mapping, namely, the clustering method of resources. In order to solve the problem of irregular dynamic change of aiNet network, the optimization objective function is defined to put forward the improved algorithm of aiNet immune learning network. The instance validation is done, the time complexity and rationality of algorithm is evaluated. At same time, the thesis researches the semantic ontology mapping method of manufacturing resources and virtual resources, and analyzes the mapping realization principle on basis of the semantic equivalent.According to the features of manufacturing task and cloud manufacturing, based on cloud pool of resources, the thesis researches the resource discovery process in the process of manufacturing, establishing the formalized model of manufacturing task, expounding the decomposition principle of task. In order to ensure the sub task set to obtain the optimal set of resources, to focus on the research of optimal of selection method of manufacturing resource, the optimization evaluation index system is established. Using the fuzzy consistent comparison matrix of hierarchy process to evaluate the weight of evaluation index, the evaluation vector is given. Under the cloud manufacturing environment, the serial and parallel processing exists in the chain of manufacturing tasks, and manufacturing resources are shared in the whole of country. So, the time and cost of logistics and warehousing are added into the evaluation index. On the basis of the evaluation index system, setting up the optimization objective function of resource with the constraint. Based on the search capability of global and the concept of Pareto set of genetic algorithm, realizing the design of resource optimization algorithm, the algorithm process is given.Finally through the virtual prototype system of cutting manufacturing resource and bearing components processing example, the above theory is verified.